Todays high performance architectures provide a challenging and highly complex environment for optimal application design. Systems consist not only of a large number of nodes connected through a complex network, but also each node contains a growing number of processors and/or cores. Understanding or predicting the performance of applications on these machines is non-trivial and in many cases even counterintuitive.

In this talk I will give an overview of two performance modeling techniques that help users better understand and optimize the scaling behavior of their codes. The first one, Concurrency Throttling, is an adaptive technique to model and adjust the optimal number of cores for a specific multi-threaded application within a node; the second one models the scaling behavior of applications based on performance data from a small set of nodes. Together, these two approaches cover both scaling dimensions of current and future machines and with that provide users with a valuable insight into their codes' performance.

Short Bio:
Martin is a Computer Scientist at the Center for Applied Scientific Computing (CASC) at Lawrence Livermore National Laboratory (LLNL). He earned his Doctorate in Computer Science in 2001 from the Technische Universitaet Muenchen (Munich, Germany). He also holds a Master of Science in Computer Science from the University of Illinois at Urbana Champaign. After completing his graduate studies and a postdoctoral appointment in Munich, he worked for two years as a Research Associate at Cornell University, before joining LLNL in 2004.

Martin's research interests include parallel and distributed architectures and applications; performance monitoring, modeling and analysis; memory system optimization; parallel programming paradigms; tool support for parallel programming; power efficiency for parallel systems; and fault tolerance at the application and system level. In his position at LLNL he especially focuses on the issue of scalability for parallel applications, code correctness tools, and parallel performance analyzers. Martin has won several awards, including the prestigious Gordon Bell Prize in 2006. He is a member of the ACM and the IEEE Computer Society.